Topic 1
Question #3
You need to build a chatbot that meets the following requirements:
✑
Supports chit-chat, knowledge base, and multilingual models
✑
Performs sentiment analysis on user messages
✑
Selects the best language model automatically
What should you integrate into the chatbot?
A. QnA Maker, Language Understanding, and Dispatch
B. Translator, Speech, and Dispatch
C. Language Understanding, Text Analytics, and QnA Maker
D. Text Analytics, Translator, and Dispatch
Correct Answer:
C
Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.
QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data.
It is used to nd the most appropriate answer for any input from your custom knowledge base (KB) of information.
Text Analytics: Mine insights in unstructured text using natural language processing (NLP)
ג
€"no machine learning expertise required. Gain a
deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API
evaluates text input
Incorrect Answers:
A, B, D: Dispatch uses sample utterances for each of your bot's different tasks (LUIS, QnA Maker, or custom), and builds a model that can be
used to properly route your user's request to the right task, even across multiple bots.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/ https://docs.microsoft.com/en-us/azure/cognitive-
services/qnamaker/overview/overview
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Topic 1
Question #4
Your company wants to reduce how long it takes for employees to log receipts in expense reports. All the receipts are in English.
You need to extract top-level information from the receipts, such as the vendor and the transaction total. The solution must minimize development
effort.
Which Azure service should you use?
A. Custom Vision
B. Personalizer
C. Form Recognizer
D. Computer Vision
Correct Answer:
C
Azure Form Recognizer is a cognitive service that lets you build automated data processing software using machine learning technology.
Identify and extract text, key/value pairs, selection marks, tables, and structure from your documents
ג
€"the service outputs structured data that
includes the relationships in the original le, bounding boxes, condence and more.
Form Recognizer is composed of custom document processing models, prebuilt models for invoices, receipts, IDs and business cards, and the
layout model.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/form-recognizer
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